The present invention relates to robotics, and in particular to apparatus controlled by a computer simulated neuronal network. As used herein the term robotics refers not only to completed robots capable of performing physical tasks but also to an apparatus which provides output information that may be used as an input by, for example, unintelligent electromotive devices. The invention enables such apparatus to distinguish simple figures from a background on the basis of the movement of the figure relative to the background. This is an important step towards an artificial vision system capable of segmenting complex visual scenes into discrete components such as objects. The present invention does not assume in advance any particular configuration for the moving figure or the background. In effect it quickly adapts to the shape of the moving figure in ways suggestive of the actual functioning of real nervous sytems.
There have been attempts to simulate intelligence systems capable of visual scene segmentation. The present invention deals with a fundamental aspect of this task, perceptional grouping and figure-ground segreation. This refers to the ability to group together elementary features into discrete objects and to segregate these objects from each other and from the background. Psychologists have identified fundamental aspects of such perceptual grouping and segmentation, such as those of similarity, continuity, proximity, and common motion. To mention an example taken from human psychology, if, within an array of moving elements, some nearby elements start to move coherently with the same velocity and direction, they are immediately perceived as a single figure segregated from the background. Computer programs that directly look for such effects prove to be ineffective when confronted by slight modifications of the environment in which they were programmed to operate. A more effective way to provide these aspects is to develop a computer simulated neuronal network capable of performing grouping and segmentation. The present invention is concerned with such neuronal networks, in particular those that employ reentrant interactions along reciprocal excitatory connections among neuronal groups.
The development of apparatus for figure-ground segregation by networks has been held back because the neural basis for such grouping and segmentation is still largely unknown. Hierarchical models have been suggested, which are composed of multiple layers of specialized detectors of increasing specificity. These require an enormous number of cells and lengthy computation times and, as a result, have never resulted in an actual apparatus. Another suggestion was to simulate neurons responding to a single, coherent object and that would interact cooperatively to form an "assembly" identified by a high firing rate, while other, unrelated neurons would fire at a lower rate. However, such a system cannot deal with stimuli containing multiple different objects.
A further alternative is to rely on the belief that neurons may express their relatedness by temporal correlations of activity. Recent experimental evidence supports this hypothesis. Orientation selective and direction selective cells in the cat primary visual cortex tend to discharge in an oscillatory fashion when stimulated appropriately. The frequency of these oscillations varies stochastically around 50 Hz. However, groups of cells with non-overlapping receptive fields become tightly synchronized when they respond to a coherent stimulus such as a long light bar. Correlations are reduced or absent if two unconnected bars are used. Thus, the phase coherency of oscillatory activity in the visual cortex may be used to link together spatially separated parts of an extended contour.
The present invention is an improvement upon work published as "Reentrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity", 86 Proc. Natl. Acad. Sci. USA pp. 7265-69 (Sep. 14, 1989), (herein "Sporns et al"). In that article we examined a computer simulation of a neuronal network and observed correlations generated by reciprocal reentrant signaling between the activity of distant neuronal groups. The simulation of reentrant activity between arrays of neuronal groups selective for oriented lines and pattern motion displayed cross-correlations between groups that were responsive to different parts of a stimulus contour if there was colinearity of the stimulus. As used in that work, the term "reentry" refers to a process of ongoing dynamic signaling via reciprocal connections between distant neuronal groups.